CN112633548B - Logistics distribution path planning method and device - Google Patents
Logistics distribution path planning method and device Download PDFInfo
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Abstract
The invention relates to the technical field of path planning, in particular to a method and a device for planning a logistics distribution path, wherein the method comprises the following steps: acquiring a demand point set to be distributed, and determining the distance information of paths between every two demand points in the demand point set; determining a preset number of ants to generate a target ant colony; sequentially determining pheromone strength of paths between every two demand points in the demand point set aiming at each ant in the target ant colony; carrying out path search by using the ants according to the random number, the distance information and the pheromone strength to obtain a distribution path corresponding to the ants; and determining the optimal distribution path of the demand point set according to the distribution path corresponding to each ant in the target ant colony. The path planning method of the invention introduces random numbers when selecting paths in the searching process, can avoid falling into local optimum, improves the efficiency and accuracy of finding the optimum distribution path, and further improves the logistics distribution efficiency.
Description
Technical Field
The invention relates to the technical field of path planning, in particular to a logistics distribution path planning method and a logistics distribution path planning device.
Background
With the continuous development of economy, especially the rapid development of electronic commerce in recent years, the logistics distribution scale is rapidly enlarged. Due to the enlargement of logistics customers, the increasing complexity of transportation networks and the unreasonable distribution routes, the logistics distribution efficiency and quality of service as well as the utilization of resources are seriously affected. Therefore, how to scientifically and reasonably find the optimal distribution path, reduce the transportation cost and improve the service quality is an important research content of the logistics industry.
The logistics distribution Vehicle path planning Problem (CVRP) with capacity constraint is a mathematical NP-hard Problem, and its solving algorithm is divided into an accurate algorithm, a traditional heuristic algorithm and an intelligent optimization algorithm. The accurate algorithm can find out the global optimal solution of the problem, but the calculated amount increases exponentially along with the scale of the problem, so that the method is not suitable for solving the vehicle path planning problem of a certain scale; the traditional heuristic algorithm is an innovation on the basis of an accurate algorithm, but is still limited, and only a local optimal solution of an optimization problem can be solved; the intelligent optimization algorithm is a better solution and is more suitable for solving the path planning problem of the large-scale vehicle.
The ant colony algorithm is an intelligent optimization algorithm for solving CVRP, and its basic idea is to utilize the cooperation of a group of artificial ants to search the better solution of the optimization problem, and each ant establishes a feasible solution or partial solution according to the criterion given by the problem from the selected initial state, and all ants exchange information through pheromones, thus achieving the purpose of mutual cooperation. However, when solving the path problem, the existing ant colony algorithm can improve the speed and accuracy of finding the optimal path, but is easy to converge to a certain local extreme point too early to obtain a local optimal solution, thereby causing the problems of low logistics distribution efficiency and high distribution cost.
Disclosure of Invention
In view of the above problems in the prior art, an object of the present invention is to provide a method and an apparatus for planning a logistics distribution route, which can avoid falling into local optimization, and improve efficiency and accuracy of finding an optimal distribution route.
In order to solve the above problem, the present invention provides a logistics distribution path planning method, including:
acquiring a demand point set to be distributed, and determining the distance information of paths between every two demand points in the demand point set;
determining a preset number of ants to generate a target ant colony;
sequentially determining the pheromone intensity of the path between every two demand points in the demand point set aiming at each ant in the target ant colony; carrying out path search by using the ants according to the random number, the distance information and the pheromone strength to obtain a distribution path corresponding to the ants;
and determining the optimal distribution path of the demand point set according to the distribution path corresponding to each ant in the target ant colony.
Further, sequentially aiming at each ant in the target ant colony, determining the pheromone intensity of a path between every two demand points in the demand point set; using the ants to search paths according to the random number, the distance information and the pheromone strength, and obtaining distribution paths corresponding to the ants comprises the following steps:
acquiring the current pheromone intensity of a path between every two demand points in the demand point set;
performing multiple iterative searches by using the current ant in the target ant colony according to the random number, the distance information and the current pheromone strength to obtain a search path corresponding to the current ant;
and determining a distribution path corresponding to the current ant according to the search path.
Further, the iterative search process includes:
acquiring a first demand point where the current ant is located;
acquiring second demand points which are not visited by the current ants, and generating a demand point sequence;
determining access probability of each second demand point in the demand point sequence based on the distance information and the current pheromone strength;
generating a random number by using a random number generator, and determining a target demand point transferred by the current ant based on the random number and the access probability;
transferring the current ants from the first demand point to the target demand point.
Further, the determining the access probability of each second demand point in the sequence of demand points based on the distance information and the current pheromone strength comprises:
determining target distance information of a path between each second demand point in the demand point sequence and the first demand point;
determining a target pheromone strength of a path between the second demand point and the first demand point;
and determining the access probability of the second demand point according to the target distance information and the target pheromone strength.
Further, the generating a random number by using a random number generator, and the determining the target demand point transferred by the current ant based on the random number and the access probability includes:
generating a random number by using a random number generator;
sequentially calculating the sum of the access probabilities of the first N second demand points in the demand point sequence, wherein N is a positive integer;
and acquiring a corresponding minimum N value when the sum of the access probabilities is greater than or equal to the random number, and taking the Nth second demand point as a target demand point transferred by the current ant.
Further, the transferring the current ants from the first demand point to the target demand point comprises:
acquiring a current path where the current ant is located and a standard load of a current vehicle;
calculating the sum of the demand of each demand point in the current path and the demand of the target demand point;
and when the sum of the demand amounts is less than or equal to the standard load, transferring the current ants from the first demand point to the target demand point.
Further, the transferring the current ants from the first demand point to the target demand point comprises:
acquiring the waiting time of the target demand point;
and when the waiting time is within a preset time window, transferring the current ants from the first demand point to the target demand point.
Further, after the transferring the current ant from the first demand point to the target demand point, the method further includes:
adding the target demand point into a distributed point set of the current ants;
judging whether all demand points in the demand point set are added into the distributed point set or not;
when all demand points in the demand point set are added into the distributed point set, outputting a search path corresponding to the current ant;
and updating the pheromone strength of the path between every two demand points in the demand point set according to the search path corresponding to the current ant.
Further, the determining an optimal distribution path of the demand point set according to the distribution paths corresponding to the ants in the target ant colony includes:
calculating the fitness information of the distribution path corresponding to each ant in the target ant colony;
and taking the distribution path with the fitness information meeting the preset condition as the optimal distribution path of the demand point set.
Another aspect of the present invention provides a logistics distribution path planning apparatus, including:
the demand point set acquisition module is used for acquiring a demand point set to be distributed and determining the distance information of paths between every two demand points in the demand point set;
the target ant colony generation module is used for determining a preset number of ants and generating a target ant colony;
the path searching module is used for determining the pheromone intensity of a path between every two demand points in the demand point set aiming at each ant in the target ant colony in sequence; carrying out path search by using the ants according to the random number, the distance information and the pheromone strength to obtain a distribution path corresponding to the ants;
and the optimal path determining module is used for determining the optimal distribution path of the demand point set according to the distribution paths corresponding to the ants in the target ant colony.
Due to the technical scheme, the invention has the following beneficial effects:
(1) According to the logistics distribution path planning method, path search is carried out for a plurality of times by using each ant in the target ant colony to obtain a plurality of distribution paths, the optimal distribution path is determined, the global search capability and robustness are strong, random numbers are introduced when the paths are selected in the search process, the situation that the paths are locally optimal can be avoided, the efficiency and accuracy of finding the optimal distribution path are improved, and further the logistics distribution efficiency is improved, and the distribution cost is reduced.
(2) According to the logistics distribution path planning method, each ant is a plurality of demand points in an independent search space, parallelism is easy to realize, and the efficiency of finding the optimal distribution path can be further improved.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings used in the embodiment or the description of the prior art will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can also be derived from them without inventive effort.
Fig. 1 is a flowchart of a logistics distribution path planning method according to an embodiment of the invention;
FIG. 2 is a flow diagram of an iterative search process provided by one embodiment of the present invention;
FIG. 3 is a flow diagram of an iterative search process provided by another embodiment of the present invention;
fig. 4 is a structural diagram of a logistics distribution path planning apparatus according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Referring to the specification and fig. 1, a flow of a logistics distribution path planning method according to an embodiment of the invention is shown. As shown in fig. 1, the method may include the steps of:
s110: acquiring a demand point set to be distributed, and determining the distance information of the path between every two demand points in the demand point set.
In the embodiment of the invention, the delivery address in the order to be delivered can be used as a demand point, and the address of the delivery store can also be used as a demand point, and the demand point is used as a starting point. The distance of the path between each demand point and the distance of the path between the starting point and each demand point can be calculated respectively.
In one possible embodiment, it is also possible to determine the demand of each demand point, the number of delivery vehicles and the standard load of each delivery vehicle, the demand may be the weight or volume of the order to be delivered, etc., the demand of the starting point may be set to zero, and the standard load of the vehicle may be the maximum load weight or capacity of the vehicle, etc.
S120: and determining a preset number of ants to generate a target ant colony.
In the embodiment of the invention, a certain number of ants can be predetermined, and the ant colony algorithm is utilized to solve to obtain the optimal distribution path. The ant colony algorithm belongs to a random search algorithm, all ants (intelligent agents) act independently without a supervision mechanism, so that local optimization is avoided, and compared with other algorithms, the ant colony algorithm has low requirements on an initial route, namely the solving result of the ant colony algorithm does not depend on the selection of the initial route, manual adjustment is not needed in the searching process, and the ant colony algorithm has strong robustness and better solution searching capability.
S130: sequentially determining pheromone strength of paths between every two demand points in the demand point set aiming at each ant in the target ant colony; and utilizing the ants to search paths according to the random number, the distance information and the pheromone strength to obtain distribution paths corresponding to the ants.
In the embodiment of the invention, the optimal distribution path can be obtained by solving the ant colony algorithm based on the target ant colony, the ant colony algorithm is a cooperative algorithm, when each ant selects the path, the probability that the path with more pheromones is selected is much higher than that of the path with less pheromones, and the convergence speed of the algorithm can be accelerated by adopting a positive feedback mechanism.
In the embodiment of the invention, when the path is selected in the path searching process, a plurality of factors such as random numbers, path distances, pheromone intensities on the paths and the like can be considered, so that the path selection has randomness, and the local optimal path can be avoided to a certain extent.
In practical applications, the problem of searching for an optimal distribution path using the ant colony algorithm is actually a problem of solving an optimal solution of a data model that aims at the shortest distance or shortest time. Illustratively, assume that logistics distribution is performed using K distribution vehicles (each having a known standard load), the K-th distribution vehicle r k The number of the distributed logistics demand points is n k (assuming that there are a total of one demand point, the location and demand of each demand point are known), and the vehicle delivery route is required to be delivered with a certain index (e.g., shortest distance, shortest time, etc.) as the target. Assuming that the shortest transport distance is the target, the basic data model can be expressed as follows:
wherein the content of the first and second substances,
for distributing vehicles r k Distance of transportation, total, from demand point i-1 to demand point i k For the standard load of the kth delivery vehicle,
for delivery vehicles r k The required amount of the required point i on the delivery path.
Wherein, the formula (1) is a solution target; the expression (2) indicates that a certain delivery vehicle is used for delivering the goods; (3) The formula indicates that the sum of the demand amounts of the demand points on each path must not exceed the load of the delivery vehicle; (4) And (5) the expression indicates that each demand point can be distributed only by one distribution vehicle, and no demand point is missed in the whole distribution. And performing multiple iterations through an ant colony algorithm, solving the optimal solution of the data model, and finally determining the optimal distribution path according to the obtained optimal solution.
In the embodiment of the invention, the pheromone strength of the path between every two demand points in the demand point set is determined for each ant in the target ant colony in sequence; using the ants to perform path search according to the random number, the distance information, and the pheromone strength, and obtaining a distribution path corresponding to the ants may include:
acquiring the current pheromone intensity of a path between every two demand points in the demand point set;
performing multiple iterative searches by using the current ant in the target ant colony according to the random number, the distance information and the current pheromone strength to obtain a search path corresponding to the current ant;
and determining a distribution path corresponding to the current ant according to the search path.
Specifically, each ant in the target ant colony may be used to perform a search once to obtain a search path corresponding to the ant, and the pheromone strength of the path between each two demand points in the demand point set is updated according to the search path, so that the search path may be selected by subsequent ants. The embodiment of the invention obtains the optimal search path through a plurality of searches, has stronger global search capability, and each ant is a plurality of demand points in an independent search space, thus being easy to realize parallelism.
In one possible embodiment, referring to fig. 2 in conjunction with the description, a flow of an iterative search process provided by an embodiment of the present invention is shown, and as shown in fig. 2, the iterative search process may include:
s131: and acquiring a first demand point where the current ant is located.
S132: and acquiring a second demand point which is not visited by the current ant, and generating a demand point sequence.
In the embodiment of the invention, the current ants can start from the starting point to search the path, and when the distribution vehicle corresponding to the current path where the current ants are located is fully loaded, the current ants can be transferred to the starting point to open up a new path again until all the required points are traversed by the current ants.
S133: determining access probabilities for respective second demand points in the sequence of demand points based on the distance information and the current pheromone strengths.
In one possible embodiment, the determining access probabilities for respective second demand points in the sequence of demand points based on the distance information and the current pheromone strength may include:
determining target distance information of a path between each second demand point in the demand point sequence and the first demand point;
determining a target pheromone strength for a path between the second demand point and the first demand point;
and determining the access probability of the second demand point according to the target distance information and the target pheromone strength.
Specifically, for each second demand point in the demand point sequence, the access probability of the second demand point may be calculated according to the following formula:
wherein, tau ij (t) represents the residual quantity of information on the path from the first demand point i to the second demand point j at the moment t, namely the target pheromone intensity, if the target pheromone intensity is larger, the tau is larger ij (t) the greater the access probability of the second demand point; eta ij ＝1/d ij ，d ij Target distance information representing a path from a first demand point i to a second demand point j, η if the target distance is smaller ij The greater the access probability of the second demand point; k represents that the current ant is the kth ant; alpha and beta represent adjustment factors, which can be predetermined, for adjusting tau ij (t) and η ij The function of the two components; allowed k The paths that have not been walked by ant k are represented, and in particular, the paths that have been walked may be stored in a tabu table, by which the logic of all solutions is guaranteed to be feasible.
S134: and generating a random number by using a random number generator, and determining a target demand point transferred by the current ant based on the random number and the access probability.
In one possible embodiment, the generating a random number by using a random number generator, and the determining the target demand point transferred by the current ant based on the random number and the access probability may include:
generating a random number using a random number generator;
sequentially calculating the sum of the access probabilities of the first N second demand points in the demand point sequence, wherein N is a positive integer;
and acquiring a corresponding minimum N value when the sum of the access probabilities is greater than or equal to the random number, and taking the Nth second demand point as a target demand point transferred by the current ant.
Specifically, the sum of the access probabilities of the first N second demand points in the demand point sequence may be calculated from N =1, and when the sum of the access probabilities of the current N second demand points is greater than or equal to the random number, the nth second demand point may be taken as the target demand point. If the sum of the access probabilities of all the second demand points is still smaller than the random number, the access probability of each second demand point in the demand point sequence can be recalculated and/or the random number can be regenerated, and the target demand point transferred by the current ant can be determined based on the re-determined access probability and the random number.
S135: transferring the current ants from the first demand point to the target demand point.
In the embodiment of the present invention, after the current ant is transferred from the first demand point to the target demand point, the processes of step S131 to step S135 may be repeated until all the demand points are traversed by the current ant, and the iterative search process of the current ant may be ended.
In a possible embodiment, referring to fig. 3 in combination with the description, the transferring the current ant from the first demand point to the target demand point may include:
s1351: acquiring a current path where the current ant is located and a standard load of a current vehicle; and calculating the sum of the demand quantities of each demand point and the target demand point in the current path.
S1352: and acquiring the waiting time of the target demand point.
S1353: and when the sum of the demand amounts is less than or equal to the standard load and the waiting time is within a preset time window, transferring the current ants from the first demand point to the target demand point.
In the embodiment of the present invention, when the sum of the demand amounts is greater than the standard load or the waiting time is outside the preset time window range, the target demand point may be abandoned, the process returns to the step S131, the iterative process is executed again, the target demand point of the current ant is determined again, the current vehicle may be replaced with a new vehicle, the current ant is transferred to the starting point, and a new path is created again. For example, when the sum of the demand amounts is greater than the standard load, the process may return to step S131, re-execute the iterative process, re-determine the target demand point of the current ant, and if a certain number of iterative processes (e.g., 3) have passed or no target demand point has been determined, replace the current vehicle with a new vehicle, transfer the current ant to the starting point, and create a new path again.
In one possible embodiment, the iterative search process for the current ant may also be ended when the demand for the target demand point is greater than the standard load of all delivery vehicles (i.e., none of the delivery vehicles is loaded) or all delivery vehicles are fully loaded.
In a possible embodiment, after the transferring the current ant from the first demand point to the target demand point, the method may further include:
adding the target demand point into a distributed point set of the current ants;
judging whether all demand points in the demand point set are added into the distributed point set or not;
when all demand points in the demand point set are added into the distributed point set, outputting a search path corresponding to the current ant;
and updating the pheromone strength of the path between every two demand points in the demand point set according to the search path corresponding to the current ant.
When there are demand points in the demand point set that are not added to the distributed point set, the process may return to step S131, and continue to execute the iterative process until all demand points are added to the distributed point set, and the search process of the current ant is ended. In one possible embodiment, a maximum number of iterations may be predetermined, and when the number of times of execution of the iterative process is equal to the maximum number of iterations, the search process for the current ant may also be ended.
Specifically, when all demand points in the demand point set have been added to the distributed point set, the search process of the current ant is ended, and a search result corresponding to the current ant may be output, where the search result may include multiple search paths. After the search path of the current ant is finished, the pheromone strength of the path between every two demand points in the demand point set (including the pheromone strength of the path between the starting point and each demand point) can be updated according to a predetermined pheromone volatilization factor, a predetermined pheromone penalty factor and the like, and the updated pheromone strength is used as the pheromone strength when the next ant is searched. The distribution path corresponding to the current ant may also be determined according to the obtained search paths, for example, each search path may be used as a distribution path of a distribution vehicle.
In one possible embodiment, under a multi-Core Processor (CPU) or multiple machines, multiple threads/multiple processes may be used to search paths for ants of the target ant colony in parallel, so as to speed up the path search process.
S140: and determining the optimal distribution path of the demand point set according to the distribution path corresponding to each ant in the target ant colony.
In the embodiment of the present invention, after the search of all ants in the target ant colony is completed, a distribution path that meets preset indexes (for example, the minimum distribution distance, the minimum distribution time, and the like) may be selected from distribution paths corresponding to all ants in the target ant colony as an optimal distribution path.
In a possible embodiment, the determining the optimal distribution path of the demand point set according to the distribution paths corresponding to the ants in the target ant group may include:
calculating the fitness information of the distribution path corresponding to each ant in the target ant colony;
and taking the distribution path with the fitness information meeting the preset condition as the optimal distribution path of the demand point set.
Specifically, the fitness information may be distances of distribution paths, distribution time required for the distribution paths, and the like, the distance of the distribution path corresponding to each ant or the required distribution time may be calculated respectively, the distribution paths corresponding to the ants are sorted according to the distribution path distance or the required distribution time, and the distribution path with the shortest distribution path distance or the shortest required distribution time is used as the optimal distribution path of the demand point set.
In one possible embodiment, the following method may also be used to determine the optimal delivery path: after the search process of the current ant is finished, the adaptability value of the distribution path corresponding to the current ant can be calculated, the adaptability value corresponding to the current ant is compared with the adaptability value corresponding to the global optimal ant, and if the adaptability value corresponding to the current ant is smaller than the adaptability value corresponding to the global optimal ant, the global optimal ant can be replaced by the current optimal ant. And after the search of each ant in the target ant colony is completed, taking the finally obtained distribution path corresponding to the globally optimal ant as the optimal distribution path of the demand point set.
The practical effect of the logistics distribution path planning method provided by the embodiment of the invention is tested, and the method provided by the embodiment of the invention is applied to solving the CVRP problem, improves the vehicle loading rate by about 20%, improves the path optimization efficiency by about 30%, greatly improves the logistics distribution efficiency, saves the logistics distribution cost and brings strong economic benefit.
In summary, the logistics distribution path planning method of the invention has the following beneficial effects:
according to the logistics distribution path planning method, path search is carried out for a plurality of times by using each ant in the target ant colony to obtain a plurality of distribution paths, the optimal distribution path is determined, the global search capability and robustness are strong, random numbers are introduced when the paths are selected in the search process, the situation that the paths are locally optimal can be avoided, the efficiency and accuracy of finding the optimal distribution path are improved, and further the logistics distribution efficiency is improved, and the distribution cost is reduced. According to the logistics distribution path planning method, each ant is a plurality of demand points in an independent search space, parallelism is easy to achieve, and the efficiency of finding the optimal distribution path can be further improved.
Referring to the specification and fig. 4, a structure of a logistics distribution path planning apparatus according to an embodiment of the invention is shown. As shown in fig. 4, the apparatus may include:
a demand point set obtaining module 410, configured to obtain a demand point set to be distributed, and determine distance information of a path between every two demand points in the demand point set;
a target ant colony generating module 420, configured to determine a preset number of ants and generate a target ant colony;
a path search module 430, configured to determine, for each ant in the target ant colony, an pheromone strength of a path between each two demand points in the demand point set; carrying out path search by using the ants according to the random number, the distance information and the pheromone strength to obtain a distribution path corresponding to the ants;
an optimal path determining module 440, configured to determine an optimal distribution path of the demand point set according to a distribution path corresponding to each ant in the target ant group.
In one possible embodiment, the path searching module 430 may include:
the pheromone intensity acquisition unit is used for acquiring the current pheromone intensity of a path between every two demand points in the demand point set;
the iterative search unit is used for carrying out iterative search for multiple times by using the current ant in the target ant colony according to the random number, the distance information and the current pheromone intensity to obtain a search path corresponding to the current ant;
and the distribution path determining unit is used for determining a distribution path corresponding to the current ant according to the search path.
The foregoing description has disclosed fully preferred embodiments of the present invention. It should be noted that those skilled in the art can make modifications to the embodiments of the present invention without departing from the scope of the appended claims. Accordingly, the scope of the appended claims is not to be limited to the specific embodiments described above.
Claims (10)
1. A logistics distribution path planning method is characterized by comprising the following steps:
acquiring a demand point set to be distributed, and determining the distance information of paths between every two demand points in the demand point set;
determining a preset number of ants to generate a target ant colony;
sequentially determining pheromone strength of paths between every two demand points in the demand point set aiming at each ant in the target ant colony; carrying out path search by using the ants according to the random number, the distance information and the pheromone strength to obtain a distribution path corresponding to the ants; after the search path of the current ant in the target ant colony is finished, updating the pheromone intensity of the path between every two demand points in the demand point set according to a predetermined pheromone volatilization factor and a predetermined pheromone punishment factor, and using the updated pheromone intensity as the pheromone intensity when the next ant is searched;
and determining the optimal distribution path of the demand point set according to the distribution paths corresponding to all the ants in the target ant colony.
2. The method as claimed in claim 1, wherein the determining, for each ant in the target ant colony in turn, an pheromone strength of a path between each demand point in the set of demand points; using the ants to search paths according to the random number, the distance information and the pheromone strength, and obtaining distribution paths corresponding to the ants comprises the following steps:
acquiring the current pheromone intensity of a path between every two demand points in the demand point set;
performing multiple iterative searches by using the current ant in the target ant colony according to a random number, the distance information and the current pheromone intensity to obtain a search path corresponding to the current ant;
and determining a distribution path corresponding to the current ant according to the search path.
3. The method of claim 2, wherein the iterative search process comprises:
acquiring a first demand point where the current ant is located;
acquiring second demand points which are not visited by the current ants, and generating a demand point sequence;
determining access probability of each second demand point in the demand point sequence based on the distance information and the current pheromone strength;
generating a random number by using a random number generator, and determining a target demand point transferred by the current ant based on the random number and the access probability;
transferring the current ant from the first demand point to the target demand point.
4. The method of claim 3, wherein said determining a probability of visit for each second demand point in the sequence of demand points based on the distance information and the current pheromone strength comprises:
determining target distance information of a path between each second demand point and the first demand point according to each second demand point in the demand point sequence;
determining a target pheromone strength of a path between the second demand point and the first demand point;
and determining the access probability of the second demand point according to the target distance information and the target pheromone strength.
5. The method according to claim 3 or 4, wherein the generating a random number by using a random number generator, and the determining the target demand point for the current ant transfer based on the random number and the access probability comprises:
generating a random number by using a random number generator;
sequentially calculating the sum of the access probabilities of the first N second demand points in the demand point sequence, wherein N is a positive integer;
and acquiring a corresponding minimum N value when the sum of the access probabilities is greater than or equal to the random number, and taking the Nth second demand point as a target demand point transferred by the current ant.
6. The method as claimed in claim 3, wherein said transferring said current ants from said first demand point to said target demand point comprises:
acquiring a current path of the current ant and a standard load of the current vehicle;
calculating the sum of the demand quantities of each demand point and the target demand point in the current path;
and when the sum of the demand amounts is less than or equal to the standard load, transferring the current ants from the first demand point to the target demand point.
7. The method as claimed in claim 3 or 6, wherein said transferring the current ant from the first demand point to the target demand point comprises:
acquiring the waiting time of the target demand point;
and when the waiting time is within a preset time window, transferring the current ants from the first demand point to the target demand point.
8. The method as claimed in claim 3 or 6, wherein after transferring the current ant from the first demand point to the target demand point, further comprising:
adding the target demand point into the distributed point set of the current ants;
judging whether all demand points in the demand point set are added into the distributed point set or not;
when all demand points in the demand point set are added into the distributed point set, outputting a search path corresponding to the current ant;
and updating the pheromone strength of the path between every two demand points in the demand point set according to the search path corresponding to the current ant.
9. The method as claimed in claim 1 or 2, wherein the determining the optimal distribution path of the demand point set according to the distribution paths corresponding to the ants in the target ant group comprises:
calculating the fitness information of the distribution path corresponding to each ant in the target ant colony;
and taking the distribution path with the fitness information meeting the preset condition as the optimal distribution path of the demand point set.
10. A logistics distribution path planning device is characterized by comprising:
the demand point set acquisition module is used for acquiring a demand point set to be distributed and determining the distance information of paths between every two demand points in the demand point set;
the target ant colony generation module is used for determining a preset number of ants to generate a target ant colony;
the path searching module is used for determining the pheromone intensity of a path between every two demand points in the demand point set aiming at each ant in the target ant colony in sequence; carrying out path search by using the ants according to the random number, the distance information and the pheromone strength to obtain a distribution path corresponding to the ants; after the searching path of the current ant in the target ant colony is finished, updating the pheromone intensity of the path between every two demand points in the demand point set according to a predetermined pheromone volatilization factor and a predetermined pheromone punishment factor, and using the updated pheromone intensity as the pheromone intensity when the next ant searches;
and the optimal path determining module is used for determining the optimal distribution path of the demand point set according to the distribution paths corresponding to the ants in the target ant colony.
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